Papers Inductive Learning
“Inductive Learning” 태그가 달린 논문 153편 · 필터 해제
A New Interpolation Approach and Corresponding Instance-Based Learning
Starting from finding approximate value of a function, introduces the measure of approximation-degree between two numerical values, proposes the concepts of "strict approximation" and "strict approximation region", then,…
Cloud ComputingInductive LearningJoint Inductive and Transductive Learning for Video Object Segmentation
Semi-supervised video object segmentation is a task of segmenting the target object in a video sequence given only a mask annotation in the first frame. The limited information available makes it an extremely challenging…
Inductive LearningObjectSemantic SegmentationSemi-Supervised Video Object Segmentation+3Multi-modal Graph Learning for Disease Prediction
Benefiting from the powerful expressive capability of graphs, graph-based approaches have achieved impressive performance in various biomedical applications. Most existing methods tend to define the adjacency matrix amon…
Disease PredictionGraph LearningGraph Representation LearningInductive Learning+2RRULES: An improvement of the RULES rule-based classifier
RRULES is presented as an improvement and optimization over RULES, a simple inductive learning algorithm for extracting IF-THEN rules from a set of training examples. RRULES optimizes the algorithm by implementing a more…
Inductive LearningCross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling
Vast amount of data generated from networks of sensors, wearables, and the Internet of Things (IoT) devices underscores the need for advanced modeling techniques that leverage the spatio-temporal structure of decentraliz…
Federated LearningGraph Neural NetworkInductive LearningSpatio-Temporal ForecastingAdversarially Regularized Graph Attention Networks for Inductive Learning on Partially Labeled Graphs
The high cost of data labeling often results in node label shortage in real applications. To improve node classification accuracy, graph-based semi-supervised learning leverages the ample unlabeled nodes to train togethe…
Graph AttentionGraph EmbeddingInductive LearningNode ClassificationInductive Topic Variational Graph Auto-Encoder for Text Classification
Graph convolutional networks (GCNs) have been applied recently to text classification and produced an excellent performance. However, existing GCN-based methods do not assume an explicit latent semantic structure of docu…
ClassificationInductive LearningRepresentation LearningSemi-Supervised Text Classification+2DOC3-Deep One Class Classification using Contradictions
This paper introduces the notion of learning from contradictions (a.k.a Universum learning) for deep one class classification problems. We formalize this notion for the widely adopted one class large-margin loss, and pro…
Anomaly DetectionClassificationInductive LearningOne-Class ClassificationReducing Bus Bunching with Asynchronous Multi-Agent Reinforcement Learning
The bus system is a critical component of sustainable urban transportation. However, due to the significant uncertainties in passenger demand and traffic conditions, bus operation is unstable in nature and bus bunching h…
Graph AttentionInductive LearningMulti-agent Reinforcement Learningreinforcement-learning+2Inductive Inference in Supervised Classification
Inductive inference in supervised classification context constitutes to methods and approaches to assign some objects or items into different predefined classes using a formal rule that is derived from training data and …
ClassificationGeneral ClassificationInductive LearningInductive Relation Prediction by BERT
Relation prediction in knowledge graphs is dominated by embedding based methods which mainly focus on the transductive setting. Unfortunately, they are not able to handle inductive learning where unseen entities and rela…
Few-Shot LearningInductive LearningInductive Relation PredictionKnowledge Graphs+6Knowledge-Preserving Incremental Social Event Detection via Heterogeneous GNNs
Social events provide valuable insights into group social behaviors and public concerns and therefore have many applications in fields such as product recommendation and crisis management. The complexity and streaming na…
Event DetectionFeature EngineeringGraph ClusteringGraph Neural Network+4A Unifying Generative Model for Graph Learning Algorithms: Label Propagation, Graph Convolutions, and Combinations
Semi-supervised learning on graphs is a widely applicable problem in network science and machine learning. Two standard algorithms -- label propagation and graph neural networks -- both operate by repeatedly passing info…
Graph LearningInductive LearningInductive Collaborative Filtering via Relation Graph Learning
Collaborative filtering has shown great power in predicting potential user-item ratings by factorizing an observed user-item rating matrix into products of two sets of latent factors. However, the user-specific latent fa…
Collaborative FilteringGraph LearningInductive LearningMatrix Completion+1GAIN: Graph Attention & Interaction Network for Inductive Semi-Supervised Learning over Large-scale Graphs
Graph Neural Networks (GNNs) have led to state-of-the-art performance on a variety of machine learning tasks such as recommendation, node classification and link prediction. Graph neural network models generate node embe…
Graph AttentionGraph Neural NetworkInductive LearningLink Prediction+1Inductive Learning on Commonsense Knowledge Graph Completion
Commonsense knowledge graph (CKG) is a special type of knowledge graph (KG), where entities are composed of free-form text. However, most existing CKG completion methods focus on the setting where all the entities are pr…
DecoderEntity EmbeddingsInductive LearningKnowledge Graph Completion+2GTEA: Inductive Representation Learning on Temporal Interaction Graphs via Temporal Edge Aggregation
In this paper, we propose the Graph Temporal Edge Aggregation (GTEA) framework for inductive learning on Temporal Interaction Graphs (TIGs). Different from previous works, GTEA models the temporal dynamics of interaction…
Inductive LearningNode ClassificationRepresentation LearningTime Series AnalysisOnline Disease Self-diagnosis with Inductive Heterogeneous Graph Convolutional Networks
We propose a Healthcare Graph Convolutional Network (HealGCN) to offer disease self-diagnosis service for online users based on Electronic Healthcare Records (EHRs). Two main challenges are focused in this paper for onli…
Graph Representation LearningInductive LearningRepresentation LearningRetrievalLearning Node Representations against Perturbations
Recent graph neural networks (GNN) has achieved remarkable performance in node representation learning. One key factor of GNN's success is the \emph{smoothness} property on node representations. Despite this, most GNN mo…
Contrastive LearningInductive LearningNode ClassificationRepresentation LearningfuncGNN: A Graph Neural Network Approach to Program Similarity
Program similarity is a fundamental concept, central to the solution of software engineering tasks such as software plagiarism, clone identification, code refactoring and code search. Accurate similarity estimation betwe…
Code SearchGraph EmbeddingGraph Neural NetworkGraph Similarity+1